LabelMe: A Database and Web-Based Tool for Image Annotation

We seek to build a large collection of images with ground truth labels to be used for object detection and recognition research. Such data is useful for supervised learning and quantitative evaluation. To achieve this, we developed a web-based tool that allows easy image annotation and instant shari...

Full description

Saved in:
Bibliographic Details
Published in:International journal of computer vision 2008-05, Vol.77 (1-3), p.157-173
Main Authors: Russell, Bryan C., Torralba, Antonio, Murphy, Kevin P., Freeman, William T.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Imaging
Computer Science
Datasets
Image Processing and Computer Vision
Pattern Recognition
Pattern Recognition and Graphics
Vision
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We seek to build a large collection of images with ground truth labels to be used for object detection and recognition research. Such data is useful for supervised learning and quantitative evaluation. To achieve this, we developed a web-based tool that allows easy image annotation and instant sharing of such annotations. Using this annotation tool, we have collected a large dataset that spans many object categories, often containing multiple instances over a wide variety of images. We quantify the contents of the dataset and compare against existing state of the art datasets used for object recognition and detection. Also, we show how to extend the dataset to automatically enhance object labels with WordNet, discover object parts, recover a depth ordering of objects in a scene, and increase the number of labels using minimal user supervision and images from the web.
ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-007-0090-8